About me
Goals for Today
What is R?
R Windows
RStudio Integrated Development Enviornment
Why I use R and you should too
Basic Operations
Assignment
Evaluation
Getting help within R
Types of R Objects
Types of R variables in Data Frames
Vectorized Arithmetic
Make simple R objects
Access names and types of variables in a data frame
Accessing variables in data frames
The R Workspace
Getting data into R
read.table
read.spss
Read and combine many files
Getting data out of R
Data management: Recoding
Data management: Merging
Data management: Subsetting
Data management: Reshaping, Wide to Long
Data management: Reshaping, Long to Wide
Reshaping with data.table
Data management: Aggregating
Data Management: Set Operations
Data Management: Character Manipulation
Data Management: dplyr package
Houston flights data
Using filter() to select observations
Using select() to select variables
Using mutate() to create new variables
Using summarize()
Using group_by() and summarize() to summarize by group
Notice group_by() and summarize() essentially aggregate
Putting data into a desired order with arrange()
Benefits of Piping
Plots: Histogram
Plots: Bar
Plots: Scatter
Plots: Scatter Enhanced
Plots: Scatter with Facets
Plots: Boxplots
Plots: Alpha Transparency
Plots: Networks
Plots: Maps
Plots: More customized
Analysis: descriptive statistics
Analysis: frequencies
Analysis: crosstabulation
More detailed descriptive statistics for quantitative variables
Analysis: t-tests
Analysis: Correlation
Analysis: Linear Regression
Analysis: Logistic Regression
Download and Plot Fitbit Data - All in R
Extracting Data from Google Scholar
Generating Non-Random Data
Generating Random Data
More on R Packages
Where and how to find R packages
How to install and load R packages
Resources to Learn More
Local R Interest Group?
That’s it for today, and thank you!

About me

Senior Research Scientist and Biostatistician
Center for Drug Use and HIV Research
Research and Methods Interests
- HIV prevention and care
- health disparities
- meta-analysis
- longitudinal data analysis
Contact
- email: cmc13@nyu.edu
- phone: 212-992-9417
- location: 433 First Ave., Room 737

Goals for Today

What is R
Why I use R and you should too
Getting data into and out of R
Data management with R
Making plots with R
Common statistical analyses done in R (e.g., t-tests, regression)
R packages
Resources to learn more and the possibility of an ongoing R interest group

What is R?

A statistical computing language and environment
Windows, Mac, Unix, Linux
Open source
Organized into packages of functions and other objects

R Windows

RStudio Integrated Development Enviornment

Why I use R and you should too

Reproducible research
- R Markdown
- LaTeX
Excellent, highly customizable graphics
Hard to find a problem/task not already solved in R
Extensive support for building new tools to extend what R can do
- Write your own functions
Open source means you can see exactly how something is done
Always will be free
BUT, not for everyone
- harder to use casually
- flexibility can be good and bad

Basic Operations

1 + 2 + 3

[1] 6

1 + 2 * 3

[1] 7

(1 + 2) * 3

[1] 9

c(0,1,2,3,5,8)

[1] 0 1 2 3 5 8

1:11

 [1]  1  2  3  4  5  6  7  8  9 10 11

1:11 + 3

 [1]  4  5  6  7  8  9 10 11 12 13 14

Assignment

x <- 3
y <- 1:10
z <- c(1,20,3,6)

Evaluation

[1] 3

 [1]  1  2  3  4  5  6  7  8  9 10

[1]  1 20  3  6

x + y

 [1]  4  5  6  7  8  9 10 11 12 13

x * z

[1]  3 60  9 18

Getting help within R

?mean
help(mean)

Types of R Objects

Data frame
- Like the typical two-dimensional table of data in other programs
Vector
matrix
- Also a two dimensional table of data, but every variable is of the same type
List
- A way to combine objects, possibly of different types, into a composite object
- Very powerful because you can apply functions to lists
- Provides ways to get a great deal accomplished with very concise code
Function

Types of R variables in Data Frames

Character

my.character <- c('Emma','Olivia','Sophia')
my.character

[1] "Emma"   "Olivia" "Sophia"

Date

my.dates <- c(as.Date("2012-04-05"), Sys.Date())
my.dates

[1] "2012-04-05" "2016-10-25"

Numeric

my.numeric <- c(12,62.3,7,101)
my.numeric

[1]  12.0  62.3   7.0 101.0

Factor

my.factor <- factor(c(0,0,0,1,1), labels=c('Female','Male'))
my.factor

[1] Female Female Female Male   Male  
Levels: Female Male

Logical

my.logical <- c(TRUE,FALSE,FALSE)
my.logical

[1]  TRUE FALSE FALSE

Vectorized Arithmetic

weight.kg <- c(60, 72, 57, 90, 95, 72)
height.meters <- c(1.75, 1.80, 1.65, 1.90, 1.74, 1.91)
bmi <- weight.kg/height.meters^2
bmi

[1] 19.59184 22.22222 20.93664 24.93075 31.37799 19.73630

Works element by element in the weight.kg and height.meters vectors
There is no need for a loop

Make simple R objects

byear <- c(68,69,71,72)
byear

[1] 68 69 71 72

Boys <- data.frame(kid = c('JHC','CMC','REC','WBC'), 
                   byear = c(68,69,70,71))
Boys

  kid byear
1 JHC    68
2 CMC    69
3 REC    70
4 WBC    71

Access names and types of variables in a data frame

names(Boys)

[1] "kid"   "byear"

str(Boys)

'data.frame':   4 obs. of  2 variables:
 $ kid  : Factor w/ 4 levels "CMC","JHC","REC",..: 2 1 3 4
 $ byear: num  68 69 70 71

Accessing variables in data frames

Boys$kid

[1] JHC CMC REC WBC
Levels: CMC JHC REC WBC

with(Boys, mean(byear))

[1] 69.5

The R Workspace

Objects you create are stored in a workspace
R allows working with multiple objects at once, not just a single dataset
Here is how to show what is in the workspace at any given point:

ls()

 [1] "bmi"           "Boys"          "byear"         "height.meters"
 [5] "my.character"  "my.dates"      "my.factor"     "my.logical"   
 [9] "my.numeric"    "weight.kg"     "x"             "y"            
[13] "z"

When you load packages, the objects in those packages are in different workspaces
I am going to load a package called foreign which has functions for reading data from and writing data to other formats

library(foreign)

I just made the objects in the foreign package available
I can see what those objects are (all functions) using a variation of the ls() command:

ls("package:foreign")

 [1] "data.restore"  "lookup.xport"  "read.arff"     "read.dbf"     
 [5] "read.dta"      "read.epiinfo"  "read.mtp"      "read.octave"  
 [9] "read.S"        "read.spss"     "read.ssd"      "read.systat"  
[13] "read.xport"    "write.arff"    "write.dbf"     "write.dta"    
[17] "write.foreign"

Getting data into R

read.table for plain text (comma or tab delimited)
read.spss for SPSS (foreign package)
read.xlsx for MS-Excel (openxlsx package)
read_sas for sas7bdat (haven package)
Many other formats and connections to databases

read.table

mytable <- read.table("c:/chuck/NYU/Stat Group/table1.txt", sep=",", header=TRUE)
mytable

  id female inc80 inc81 inc82
1  1      0  5000  5500  6000
2  2      1  2000  2200  3300
3  3      0  3000  2000  1000

str(mytable)

'data.frame':   3 obs. of  5 variables:
 $ id    : int  1 2 3
 $ female: int  0 1 0
 $ inc80 : int  5000 2000 3000
 $ inc81 : int  5500 2200 2000
 $ inc82 : int  6000 3300 1000

read.spss

library(foreign)
titanic <- read.spss("c:/chuck/NYU/Stat Group/titanic.sav", to.data.frame = TRUE)
head(titanic)

  pclass survived     age     fare gender
1  First      Yes 29.0000 211.3375 Female
2  First      Yes  0.9167 151.5500   Male
3  First       No  2.0000 151.5500 Female
4  First       No 30.0000 151.5500   Male
5  First       No 25.0000 151.5500 Female
6  First      Yes 48.0000  26.5500   Male

attributes(titanic)$variable.labels

            pclass           survived                age 
 "Passenger Class"         "Survived"    "Passenger Age" 
              fare             gender 
  "Passenger Fare" "Passenger Gender"

Read and combine many files

There are 8 MS-Excel files with the same structure in a folder
Each has 10 observations on two variables, X and Y
First, I load the package needed for reading Excel files, then get a list of the file names

library(openxlsx)
my.files <- list.files("c:/chuck/NYU/Stat Group/R Workshop/Many Files/", pattern = ".xlsx$", full.names=TRUE, recursive=TRUE)
my.files # vector of the file names with full path

[1] "c:/chuck/NYU/Stat Group/R Workshop/Many Files/File1.xlsx"
[2] "c:/chuck/NYU/Stat Group/R Workshop/Many Files/File2.xlsx"
[3] "c:/chuck/NYU/Stat Group/R Workshop/Many Files/File3.xlsx"
[4] "c:/chuck/NYU/Stat Group/R Workshop/Many Files/File4.xlsx"
[5] "c:/chuck/NYU/Stat Group/R Workshop/Many Files/File5.xlsx"
[6] "c:/chuck/NYU/Stat Group/R Workshop/Many Files/File6.xlsx"
[7] "c:/chuck/NYU/Stat Group/R Workshop/Many Files/File7.xlsx"
[8] "c:/chuck/NYU/Stat Group/R Workshop/Many Files/File8.xlsx"

Next I gather the names of the worksheets within each workbook

my.sheets <- lapply(my.files, function(x){getSheetNames(x)}) # list of worksheet names within each MS-Excel workbook
unlist(my.sheets)

[1] "Sheet 1" "Sheet 1" "Sheet 1" "Sheet 1" "Sheet 1" "Sheet 1" "Sheet 1"
[8] "Sheet 1"

I put the workbook filenames and worksheet names together in a data frame

my.sheets.DF <- data.frame(file = rep(my.files, unlist(lapply(my.sheets, length))), sheet = unlist(my.sheets))
my.sheets.DF

                                                      file   sheet
1 c:/chuck/NYU/Stat Group/R Workshop/Many Files/File1.xlsx Sheet 1
2 c:/chuck/NYU/Stat Group/R Workshop/Many Files/File2.xlsx Sheet 1
3 c:/chuck/NYU/Stat Group/R Workshop/Many Files/File3.xlsx Sheet 1
4 c:/chuck/NYU/Stat Group/R Workshop/Many Files/File4.xlsx Sheet 1
5 c:/chuck/NYU/Stat Group/R Workshop/Many Files/File5.xlsx Sheet 1
6 c:/chuck/NYU/Stat Group/R Workshop/Many Files/File6.xlsx Sheet 1
7 c:/chuck/NYU/Stat Group/R Workshop/Many Files/File7.xlsx Sheet 1
8 c:/chuck/NYU/Stat Group/R Workshop/Many Files/File8.xlsx Sheet 1

I create an empty list to hold each data file

my.list <- vector(mode="list", length = nrow(my.sheets.DF)) # empty list to hold each data file

I loop over the file and worksheet names, reading each in and putting it into the list

for(i in 1:nrow(my.sheets.DF)){
  my.list[[i]] <- read.xlsx(xlsxFile = as.character(my.sheets.DF$file)[i], sheet = as.character(my.sheets.DF$sheet)[i])  
}

Finally, I combine the 8 data frames into one big data frame

my.DF <- as.data.frame(do.call(rbind, my.list))
dim(my.DF) # 80 observations, 2 variables

[1] 80  2

The example has just 8 files in the folder, but it could be thousands and the principle is the same

Getting data out of R

write.table for plain text (comma or tab delimited)
write.spss for SPSS (foreign package)
write.dta for Stata (foreign package)
write.xlsx for MS-Excel (openxlsx package)

Data management: Recoding

library(foreign)
library(car)
auto <- read.spss("c:/chuck/NYU/Stat Group/R Workshop/auto.sav", to.data.frame=TRUE)
summary(auto$MPG)
auto$MPG3 <- car::recode(auto$MPG, "lo:18=1; 19:23=2; 24:hi=3")

table(auto$MPG3)


 1  2  3 
27 24 23

Data management: Merging

Two data frames with a common ID variable

A <- data.frame(famid = c(2,1,3), 
                name = c('Art','Bill','Paul'), 
                inc = c(22,30,25))
B <- data.frame(famid = c(3,1,2), 
                faminc96 = c(75,40,45), 
                faminc97 = c(76,40.5,40.4), 
                faminc98 = c(77,41,45.8))
intersect(names(A), names(B))

[1] "famid"

One Merged Data Frame

  famid name inc
1     2  Art  22
2     1 Bill  30
3     3 Paul  25

  famid faminc96 faminc97 faminc98
1     3       75     76.0     77.0
2     1       40     40.5     41.0
3     2       45     40.4     45.8

AB <- merge(A, B)
AB

  famid name inc faminc96 faminc97 faminc98
1     1 Bill  30       40     40.5     41.0
2     2  Art  22       45     40.4     45.8
3     3 Paul  25       75     76.0     77.0

Data management: Subsetting

bank <- read.spss("c:/chuck/NYU/Stat Group/bank.sav", to.data.frame = TRUE)
head(bank)

   ID SALBEG   SEX TIME   AGE SALNOW EDLEVEL  WORK          JOBCAT
1 628   8400 Males   81 28.50  16080      16  0.25 College trainee
2 630  24000 Males   73 40.33  41400      16 12.50 Exempt employee
3 632  10200 Males   83 31.08  21960      15  4.08 Exempt employee
4 633   8700 Males   93 31.17  19200      16  1.83 College trainee
5 635  17400 Males   83 41.92  28350      19 13.00 Exempt employee
6 637  12996 Males   80 29.50  27250      18  2.42 College trainee
  MINORITY     SEXRACE EGENDER EMINORIT EGENXMIN BSALCENT   CSALADJ
1    White White males      -1       -1        1  1593.57 13133.489
2    White White males      -1       -1        1 17193.57  9609.089
3    White White males      -1       -1        1  3393.57 15685.289
4    White White males      -1       -1        1  1893.57 15698.789
5    White White males      -1       -1        1 10593.57  8762.489
6    White White males      -1       -1        1  6189.57 15805.485
  SALDELTA     RES_1 GENDER
1     7680 -1013.504      M
2    17400 -4543.634      M
3    11760  1537.635      M
4    10500  1551.686      M
5    10950 -5387.809      M
6    14254  1656.804      M

The head() function is a convenient way to see the first few observations of a data object
With the subset() function

subset(bank, SALBEG < 4000 & SEX == "Males", select=c(ID,SEX,SALBEG,AGE,SALNOW))

      ID   SEX SALBEG   AGE SALNOW
414  926 Males   3600 53.50  12300
429 1077 Males   3900 29.17   9000

Or by indexing

bank[bank$SALBEG < 4000 & bank$SEX == "Males", c(1,3,2,5,6)]

      ID   SEX SALBEG   AGE SALNOW
414  926 Males   3600 53.50  12300
429 1077 Males   3900 29.17   9000

In both cases, I selected males with a beginning salary less than 4000, and only certain variables
- subset specified the variables to include by name
- indexing specified the variables to include based on their location in the data frame

Data management: Reshaping, Wide to Long

mytable <- read.table("c:/chuck/NYU/Stat Group/table1.txt", sep=",", header=TRUE)
mytable

  id female inc80 inc81 inc82
1  1      0  5000  5500  6000
2  2      1  2000  2200  3300
3  3      0  3000  2000  1000

library(dplyr)
library(tidyr)
longtab <- gather(mytable, year, inc, inc80:inc82)
longtab

  id female  year  inc
1  1      0 inc80 5000
2  2      1 inc80 2000
3  3      0 inc80 3000
4  1      0 inc81 5500
5  2      1 inc81 2200
6  3      0 inc81 2000
7  1      0 inc82 6000
8  2      1 inc82 3300
9  3      0 inc82 1000

Data management: Reshaping, Long to Wide

longtab

  id female  year  inc
1  1      0 inc80 5000
2  2      1 inc80 2000
3  3      0 inc80 3000
4  1      0 inc81 5500
5  2      1 inc81 2200
6  3      0 inc81 2000
7  1      0 inc82 6000
8  2      1 inc82 3300
9  3      0 inc82 1000

widetab <- spread(longtab, year, inc)
widetab

  id female inc80 inc81 inc82
1  1      0  5000  5500  6000
2  2      1  2000  2200  3300
3  3      0  3000  2000  1000

Reshaping with data.table

Data management: Aggregating

head(iris)

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

aggregate(iris[,1:4], list(Species = iris$Species), FUN = mean)

     Species Sepal.Length Sepal.Width Petal.Length Petal.Width
1     setosa        5.006       3.428        1.462       0.246
2 versicolor        5.936       2.770        4.260       1.326
3  virginica        6.588       2.974        5.552       2.026

Data Management: Set Operations

x <- c('A','B','C','D')
y <- c('D','A','F','J')
setdiff(x, y) # which elements of x are not in y

[1] "B" "C"

setdiff(y, x) # which elements of y are not in x

[1] "F" "J"

intersect(x, y) # what elements are in both x and y

[1] "A" "D"

union(x, y) # elements in either x or y

[1] "A" "B" "C" "D" "F" "J"

"C" %in% x # is there a "C" in x

[1] TRUE

y %in% c("F","J") # which elements of y are equal to "F" or "J"

[1] FALSE FALSE  TRUE  TRUE

Data Management: Character Manipulation

x <- c('A','B','C','D')
grep('C', x)

[1] 3

gsub('C', 'c', x)

[1] "A" "B" "c" "D"

paste('Item', 1:10, sep="-")

 [1] "Item-1"  "Item-2"  "Item-3"  "Item-4"  "Item-5"  "Item-6"  "Item-7" 
 [8] "Item-8"  "Item-9"  "Item-10"

x <- c('fruit_apple','instrument_guitar','instrument_piano','fruit_orange','instrument_trumpet')
grepl("fruit", x)

[1]  TRUE FALSE FALSE  TRUE FALSE

Data Management: dplyr package

library(dplyr)
filter()
select()
mutate()
summarize()
group_by()
arrange()

Houston flights data

All flights departing from Houston airports IAH and HOU
Research and Innovation Technology Administration at the Bureau of Transporation Statistics

library(hflights)
head(hflights)

     Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier
5424 2011     1          1         6    1400    1500            AA
5425 2011     1          2         7    1401    1501            AA
5426 2011     1          3         1    1352    1502            AA
5427 2011     1          4         2    1403    1513            AA
5428 2011     1          5         3    1405    1507            AA
5429 2011     1          6         4    1359    1503            AA
     FlightNum TailNum ActualElapsedTime AirTime ArrDelay DepDelay Origin
5424       428  N576AA                60      40      -10        0    IAH
5425       428  N557AA                60      45       -9        1    IAH
5426       428  N541AA                70      48       -8       -8    IAH
5427       428  N403AA                70      39        3        3    IAH
5428       428  N492AA                62      44       -3        5    IAH
5429       428  N262AA                64      45       -7       -1    IAH
     Dest Distance TaxiIn TaxiOut Cancelled CancellationCode Diverted
5424  DFW      224      7      13         0                         0
5425  DFW      224      6       9         0                         0
5426  DFW      224      5      17         0                         0
5427  DFW      224      9      22         0                         0
5428  DFW      224      9       9         0                         0
5429  DFW      224      6      13         0                         0

str(hflights)

'data.frame':   227496 obs. of  21 variables:
 $ Year             : int  2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 ...
 $ Month            : int  1 1 1 1 1 1 1 1 1 1 ...
 $ DayofMonth       : int  1 2 3 4 5 6 7 8 9 10 ...
 $ DayOfWeek        : int  6 7 1 2 3 4 5 6 7 1 ...
 $ DepTime          : int  1400 1401 1352 1403 1405 1359 1359 1355 1443 1443 ...
 $ ArrTime          : int  1500 1501 1502 1513 1507 1503 1509 1454 1554 1553 ...
 $ UniqueCarrier    : chr  "AA" "AA" "AA" "AA" ...
 $ FlightNum        : int  428 428 428 428 428 428 428 428 428 428 ...
 $ TailNum          : chr  "N576AA" "N557AA" "N541AA" "N403AA" ...
 $ ActualElapsedTime: int  60 60 70 70 62 64 70 59 71 70 ...
 $ AirTime          : int  40 45 48 39 44 45 43 40 41 45 ...
 $ ArrDelay         : int  -10 -9 -8 3 -3 -7 -1 -16 44 43 ...
 $ DepDelay         : int  0 1 -8 3 5 -1 -1 -5 43 43 ...
 $ Origin           : chr  "IAH" "IAH" "IAH" "IAH" ...
 $ Dest             : chr  "DFW" "DFW" "DFW" "DFW" ...
 $ Distance         : int  224 224 224 224 224 224 224 224 224 224 ...
 $ TaxiIn           : int  7 6 5 9 9 6 12 7 8 6 ...
 $ TaxiOut          : int  13 9 17 22 9 13 15 12 22 19 ...
 $ Cancelled        : int  0 0 0 0 0 0 0 0 0 0 ...
 $ CancellationCode : chr  "" "" "" "" ...
 $ Diverted         : int  0 0 0 0 0 0 0 0 0 0 ...

Using filter() to select observations

January flights by American Airlines

head(filter(hflights, Month == 1, UniqueCarrier == "AA"))

  Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier FlightNum
1 2011     1          1         6    1400    1500            AA       428
2 2011     1          2         7    1401    1501            AA       428
3 2011     1          3         1    1352    1502            AA       428
4 2011     1          4         2    1403    1513            AA       428
5 2011     1          5         3    1405    1507            AA       428
6 2011     1          6         4    1359    1503            AA       428
  TailNum ActualElapsedTime AirTime ArrDelay DepDelay Origin Dest Distance
1  N576AA                60      40      -10        0    IAH  DFW      224
2  N557AA                60      45       -9        1    IAH  DFW      224
3  N541AA                70      48       -8       -8    IAH  DFW      224
4  N403AA                70      39        3        3    IAH  DFW      224
5  N492AA                62      44       -3        5    IAH  DFW      224
6  N262AA                64      45       -7       -1    IAH  DFW      224
  TaxiIn TaxiOut Cancelled CancellationCode Diverted
1      7      13         0                         0
2      6       9         0                         0
3      5      17         0                         0
4      9      22         0                         0
5      9       9         0                         0
6      6      13         0                         0

Using select() to select variables

Just the Departure Time, Arrival Time, and Air Time variables

head(select(hflights, DepTime, ArrTime, AirTime))

     DepTime ArrTime AirTime
5424    1400    1500      40
5425    1401    1501      45
5426    1352    1502      48
5427    1403    1513      39
5428    1405    1507      44
5429    1359    1503      45

All variables that end with Time

head(select(hflights, ends_with("Time")))

     DepTime ArrTime ActualElapsedTime AirTime
5424    1400    1500                60      40
5425    1401    1501                60      45
5426    1352    1502                70      48
5427    1403    1513                70      39
5428    1405    1507                62      44
5429    1359    1503                64      45

Using mutate() to create new variables

g1 <- mutate(hflights, ActualGroundTime = ActualElapsedTime - AirTime)
head(select(g1, ActualElapsedTime, AirTime, ActualGroundTime))

  ActualElapsedTime AirTime ActualGroundTime
1                60      40               20
2                60      45               15
3                70      48               22
4                70      39               31
5                62      44               18
6                64      45               19

Using summarize()

summarize(hflights, min_dist = min(Distance),
                    mean_dist = mean(Distance),
                    median_dist = median(Distance),
                    sd_dist = sd(Distance),
                    max_dist = max(Distance))

  min_dist mean_dist median_dist  sd_dist max_dist
1       79  787.7832         809 453.6806     3904

Using group_by() and summarize() to summarize by group

This also illustrates the use of pipes in R

hflights %>% group_by(UniqueCarrier) %>%
summarize(min_dist = min(Distance),
          mean_dist = mean(Distance),
          median_dist = median(Distance),
          sd_dist = sd(Distance),
          max_dist = max(Distance))

# A tibble: 15 × 6
   UniqueCarrier min_dist mean_dist median_dist    sd_dist max_dist
           <chr>    <int>     <dbl>       <dbl>      <dbl>    <int>
1             AA      224  483.8212         224 353.269167      964
2             AS     1874 1874.0000        1874   0.000000     1874
3             B6     1428 1428.0000        1428   0.000000     1428
4             CO      140 1098.0549        1190 505.204266     3904
5             DL      689  723.2832         689 103.886047     1076
6             EV      468  775.6815         696 259.664313     1076
7             F9      666  882.7411         883   7.496141      883
8             FL      490  685.4063         696  45.508796      696
9             MQ      247  650.5310         247 447.617384     1379
10            OO      140  819.7279         809 299.852214     1428
11            UA      643 1177.8388         925 326.355580     1635
12            US      912  981.4677         913 110.098250     1325
13            WN      148  606.6218         453 399.144719     1642
14            XE       79  589.0326         562 280.514799     1201
15            YV      912  938.6709         913  79.603621     1190

Notice group_by() and summarize() essentially aggregate

iris %>% 
  group_by(Species) %>%
  summarize(Sepal.Length = mean(Sepal.Length),
            Sepal.Width = mean(Sepal.Width),
            Petal.Length = mean(Petal.Length),
            Petal.Width = mean(Petal.Width))

# A tibble: 3 × 5
     Species Sepal.Length Sepal.Width Petal.Length Petal.Width
      <fctr>        <dbl>       <dbl>        <dbl>       <dbl>
1     setosa        5.006       3.428        1.462       0.246
2 versicolor        5.936       2.770        4.260       1.326
3  virginica        6.588       2.974        5.552       2.026

But much more flexibly!

Putting data into a desired order with arrange()

hflights %>% group_by(UniqueCarrier) %>%
summarize(min_dist = min(Distance),
          mean_dist = mean(Distance),
          median_dist = median(Distance),
          sd_dist = sd(Distance),
          max_dist = max(Distance)) %>%
arrange(mean_dist)

# A tibble: 15 × 6
   UniqueCarrier min_dist mean_dist median_dist    sd_dist max_dist
           <chr>    <int>     <dbl>       <dbl>      <dbl>    <int>
1             AA      224  483.8212         224 353.269167      964
2             XE       79  589.0326         562 280.514799     1201
3             WN      148  606.6218         453 399.144719     1642
4             MQ      247  650.5310         247 447.617384     1379
5             FL      490  685.4063         696  45.508796      696
6             DL      689  723.2832         689 103.886047     1076
7             EV      468  775.6815         696 259.664313     1076
8             OO      140  819.7279         809 299.852214     1428
9             F9      666  882.7411         883   7.496141      883
10            YV      912  938.6709         913  79.603621     1190
11            US      912  981.4677         913 110.098250     1325
12            CO      140 1098.0549        1190 505.204266     3904
13            UA      643 1177.8388         925 326.355580     1635
14            B6     1428 1428.0000        1428   0.000000     1428
15            AS     1874 1874.0000        1874   0.000000     1874

Benefits of Piping

Without Piping

arrange(
  summarize(
    group_by(
      filter(mtcars, carb > 1),
    cyl),
  Avg_mpg = mean(mpg)
  ),
desc(Avg_mpg)  
  )

# A tibble: 3 × 2
    cyl Avg_mpg
  <dbl>   <dbl>
1     4   25.90
2     6   19.74
3     8   15.10

With Piping

mtcars %>%
  filter(carb > 1) %>%
  group_by(cyl) %>%
  summarize(Avg_mpg = mean(mpg)) %>%
  arrange(desc(Avg_mpg))

# A tibble: 3 × 2
    cyl Avg_mpg
  <dbl>   <dbl>
1     4   25.90
2     6   19.74
3     8   15.10

Plots: Histogram

hsb2 <- read.table('http://www.ats.ucla.edu/stat/r/modules/hsb2.csv', header=T, sep=",")
library(ggplot2)
ggplot(hsb2, aes(x = write)) + geom_histogram()

Plots: Bar

library(openxlsx)
fname <- read.xlsx("c:/chuck/NYU/Stat Group/R Workshop/Female-Names.xlsx", sheet = 1)
fname

   Rank      Name Ngirls
1     1      Emma  20799
2     2    Olivia  19674
3     3    Sophia  18490
4     4  Isabella  16950
5     5       Ava  15586
6     6       Mia  13442
7     7     Emily  12562
8     8   Abigail  11985
9     9   Madison  10247
10   10 Charlotte  10048

ggplot(fname, aes(x = Name, y = Ngirls)) + 
  geom_bar(stat = 'identity') + 
  ylab('Number') + xlab('') + 
  ggtitle('Ten Most Common Female Baby\nNames in 2014') +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))

Plots: Scatter

ggplot2 package

ggplot(hsb2, aes(x = write, y = read)) + geom_point()

Base R

plot(read ~ write, data = hsb2)

Plots: Scatter Enhanced

library(ggplot2)
ggplot(subset(bank, SALNOW < 30000), aes(x = SALBEG, y = SALNOW, colour = GENDER)) + 
  geom_point() + scale_y_continuous(limits=c(0,30000)) + scale_x_continuous(limits=c(0,30000))

Plots: Scatter with Facets

library(ggplot2)
ggplot(subset(bank, SALNOW < 30000), aes(x = SALBEG, y = SALNOW)) + 
  geom_point() + scale_y_continuous(limits=c(0,30000)) + scale_x_continuous(limits=c(0,30000)) + facet_wrap( ~ GENDER)

Plots: Boxplots

Writing score on vertical axis

hsb <- read.dta('c:/chuck/NYU/Stat Group/R Workshop/hsb.dta')
ggplot(hsb, aes(x = female, y = write)) + geom_boxplot() + xlab("")

Flip coordinates so writing score is on the horizontal axis

ggplot(hsb, aes(x = female, y = write)) + geom_boxplot() + xlab("") + coord_flip()

Plots: Alpha Transparency

Without transparency

ggplot(myDF, aes(x=X,y=Y)) + geom_point(color="blue", size=3)

With transparency

ggplot(myDF, aes(x=X,y=Y)) + geom_point(color="blue", size=3, alpha =.20)

Plots: Networks

library(igraph)
net.bg <- barabasi.game(80) 
V(net.bg)$frame.color <- sample(c("white","black"), size = 80, replace=TRUE, prob=c(0.5,0.5))
V(net.bg)$color <- sample(c("orange","lightblue"), size = 80, replace=TRUE, prob=c(0.5,0.5))
V(net.bg)$label <- "" 
V(net.bg)$size <- 10
E(net.bg)$arrow.mode <- 0
plot(net.bg, layout=layout.lgl)

Plots: Maps

Plots: More customized

library(ggplot2) # devtools::install_github("hadley/ggplot2")
library(ggalt)   # devtools::install_github("hrbrmstr/ggalt")
library(dplyr)   # for data_frame() & arrange()

df <- data_frame(country=c("Germany", "France", "Vietnam", "Japan", "Poland", "Lebanon",
                           "Australia", "South\nKorea", "Canada", "Spain", "Italy", "Peru",
                           "U.S.", "UK", "Mexico", "Chile", "China", "India"),
                 ages_35=c(0.39, 0.42, 0.49, 0.43, 0.51, 0.57,
                           0.60, 0.45, 0.65, 0.57, 0.57, 0.65,
                           0.63, 0.59, 0.67, 0.75, 0.52, 0.48),
                 ages_18_to_34=c(0.81, 0.83, 0.86, 0.78, 0.86, 0.90,
                                 0.91, 0.75, 0.93, 0.85, 0.83, 0.91,
                                 0.89, 0.84, 0.90, 0.96, 0.73, 0.69),
                 diff=sprintf("+%d", as.integer((ages_18_to_34-ages_35)*100)))

df <- arrange(df, desc(diff))
df$country <- factor(df$country, levels=rev(df$country))

percent_first <- function(x) {
  x <- sprintf("%d%%", round(x*100))
  x[2:length(x)] <- sub("%$", "", x[2:length(x)])
  x
}

gg <- ggplot()
gg <- gg + geom_segment(data=df, aes(y=country, yend=country, x=0, xend=1), color="#b2b2b2", size=0.15)
gg <- gg + geom_segment(data=df, aes(y=country, yend=country, x=ages_35, xend=ages_18_to_34), color="#b2b2b2", size=2)
gg <- gg + geom_point(data=df, aes(x=ages_35, y = country), size=3, colour = "#9fb059")
gg <- gg + geom_point(data=df, aes(x=ages_18_to_34, y = country), size=3, colour = "#edae52")
gg <- gg + geom_text(data=dplyr::filter(df, country=="Germany"),
                     aes(x=ages_35, y=country, label="Ages 35+"),
                     color="#9fb059", size=3, vjust=-1, fontface="bold", family="Calibri")
gg <- gg + geom_text(data=dplyr::filter(df, country=="Germany"),
                     aes(x=ages_18_to_34, y=country, label="Ages 18-34"),
                     color="#edae52", size=3, vjust=-1, fontface="bold", family="Calibri")
gg <- gg + geom_text(data=df, aes(x=ages_35, y=country, label=percent_first(ages_35)),
                     color="#9fb059", size=2.75, vjust=1.75, family="Calibri")
gg <- gg + geom_text(data=df, color="#edae52", size=2.75, vjust=1.75, family="Calibri",
                     aes(x=ages_18_to_34, y=country, label=percent_first(ages_18_to_34)))
gg <- gg + geom_rect(data=df, aes(xmin=1.05, xmax=1.175, ymin=-Inf, ymax=Inf), fill="#efefe3")
gg <- gg + geom_text(data=df, aes(label=diff, y=country, x=1.1125), fontface="bold", size=3, family="Calibri")
gg <- gg + geom_text(data=filter(df, country=="Germany"), aes(x=1.1125, y=country, label="DIFF"),
                     color="#7a7d7e", size=3.1, vjust=-2, fontface="bold", family="Calibri")
gg <- gg + scale_x_continuous(expand=c(0,0), limits=c(0, 1.175))
gg <- gg + scale_y_discrete(expand=c(0.075,0))
gg <- gg + labs(x=NULL, y=NULL, title="The social media age gap",
                subtitle="Adult internet users or reported smartphone owners who\nuse social networking sites",
                caption="Source: Pew Research Center, Spring 2015 Global Attitudes Survey. Q74")
gg <- gg + theme_bw(base_family="Calibri")
gg <- gg + theme(panel.grid.major=element_blank())
gg <- gg + theme(panel.grid.minor=element_blank())
gg <- gg + theme(panel.border=element_blank())
gg <- gg + theme(axis.ticks=element_blank())
gg <- gg + theme(axis.text.x=element_blank())
gg <- gg + theme(plot.title=element_text(face="bold"))
gg <- gg + theme(plot.subtitle=element_text(face="italic", size=9, margin=margin(b=12)))
gg <- gg + theme(plot.caption=element_text(size=7, margin=margin(t=12), color="#7a7d7e"))
gg

Analysis: descriptive statistics

hsb <- read.dta('c:/chuck/NYU/Stat Group/R Workshop/hsb.dta')
summary(hsb)

       id            female              race         ses    
 Min.   :  1.00   male  : 91   hispanic    : 24   low   :47  
 1st Qu.: 50.75   female:109   asian       : 11   middle:95  
 Median :100.50                african-amer: 20   high  :58  
 Mean   :100.50                white       :145              
 3rd Qu.:150.25                                              
 Max.   :200.00                                              
     schtyp          prog          read           write      
 public :168   general : 45   Min.   :28.00   Min.   :31.00  
 private: 32   academic:105   1st Qu.:44.00   1st Qu.:45.75  
               vocation: 50   Median :50.00   Median :54.00  
                              Mean   :52.23   Mean   :52.77  
                              3rd Qu.:60.00   3rd Qu.:60.00  
                              Max.   :76.00   Max.   :67.00  
      math          science          socst      
 Min.   :33.00   Min.   :26.00   Min.   :26.00  
 1st Qu.:45.00   1st Qu.:44.00   1st Qu.:46.00  
 Median :52.00   Median :53.00   Median :52.00  
 Mean   :52.65   Mean   :51.85   Mean   :52.41  
 3rd Qu.:59.00   3rd Qu.:58.00   3rd Qu.:61.00  
 Max.   :75.00   Max.   :74.00   Max.   :71.00

Analysis: frequencies

library(descr)
with(bank, freq(JOBCAT, plot=FALSE))

JOBCAT 
                 Frequency Percent
Clerical               227  47.890
Office trainee         136  28.692
Security officer        27   5.696
College trainee         41   8.650
Exempt employee         32   6.751
MBA trainee              5   1.055
Technical                6   1.266
Total                  474 100.000

Analysis: crosstabulation

library(descr)
with(bank, CrossTable(SEX, JOBCAT %in% c("Clerical","Office trainee"), 
                    prop.r=TRUE, prop.chi=FALSE, prop.t=FALSE, prop.c=FALSE, chisq=TRUE))

.    Cell Contents 
. |-------------------------|
. |                   Count | 
. |             Row Percent | 
. |-------------------------|
. 
. ================================
.            JOBCAT %in% c("Clerical", "Office trainee")
. SEX        FALSE    TRUE   Total
. --------------------------------
. Males       101     157     258 
.            39.1%   60.9%   54.4%
. --------------------------------
. Females      10     206     216 
.             4.6%   95.4%   45.6%
. --------------------------------
. Total       111     363     474 
. ================================
. 
. Statistics for All Table Factors
. 
. Pearson's Chi-squared test 
. ------------------------------------------------------------
. Chi^2 = 78.10967      d.f. = 1      p <2e-16 
. 
. Pearson's Chi-squared test with Yates' continuity correction 
. ------------------------------------------------------------
. Chi^2 = 76.19681      d.f. = 1      p <2e-16 
.         Minimum expected frequency: 50.58228

More detailed descriptive statistics for quantitative variables

library(psych)
psych::describe(subset(hsb, select=c(read,write,math,science,socst)))

        vars   n  mean    sd median trimmed   mad min max range  skew
read       1 200 52.23 10.25     50   52.03 10.38  28  76    48  0.19
write      2 200 52.77  9.48     54   53.36 11.86  31  67    36 -0.47
math       3 200 52.65  9.37     52   52.23 10.38  33  75    42  0.28
science    4 200 51.85  9.90     53   52.02 11.86  26  74    48 -0.19
socst      5 200 52.41 10.74     52   52.99 13.34  26  71    45 -0.38
        kurtosis   se
read       -0.66 0.72
write      -0.78 0.67
math       -0.69 0.66
science    -0.60 0.70
socst      -0.57 0.76

Analysis: t-tests

Following these examples: http://www.ats.ucla.edu/stat/stata/output/ttest_output.htm
One sample t-test where hypothesized mean for write score is 50

hsb <- read.dta('c:/chuck/NYU/Stat Group/R Workshop/hsb.dta')
with(hsb, t.test(write, mu=50))


    One Sample t-test

data:  write
t = 4.1403, df = 199, p-value = 5.121e-05
alternative hypothesis: true mean is not equal to 50
95 percent confidence interval:
 51.45332 54.09668
sample estimates:
mean of x 
   52.775

Paired t-test comparing read and write scores

with(hsb, t.test(write, read, paired = TRUE))


    Paired t-test

data:  write and read
t = 0.86731, df = 199, p-value = 0.3868
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.6941424  1.7841424
sample estimates:
mean of the differences 
                  0.545

Independent-samples t-test comparing write scores of male and female students

t.test(write ~ female, data = hsb, var.equal = TRUE)


    Two Sample t-test

data:  write by female
t = -3.7341, df = 198, p-value = 0.0002463
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -7.441835 -2.298059
sample estimates:
  mean in group male mean in group female 
            50.12088             54.99083

Do not assume equal variance in each group

t.test(write ~ female, data = hsb)


    Welch Two Sample t-test

data:  write by female
t = -3.6564, df = 169.71, p-value = 0.0003409
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -7.499159 -2.240734
sample estimates:
  mean in group male mean in group female 
            50.12088             54.99083

Analysis: Correlation

hsb <- read.spss("c:/chuck/NYU/Stat Group/R Workshop/hsb2.sav", to.data.frame=TRUE)
round(cor(subset(hsb, select=c(READ,WRITE,MATH,SCIENCE,SOCST))), 2)

        READ WRITE MATH SCIENCE SOCST
READ    1.00  0.60 0.66    0.63  0.62
WRITE   0.60  1.00 0.62    0.57  0.60
MATH    0.66  0.62 1.00    0.63  0.54
SCIENCE 0.63  0.57 0.63    1.00  0.47
SOCST   0.62  0.60 0.54    0.47  1.00

cor.test(~ READ + SOCST, data = hsb)


    Pearson's product-moment correlation

data:  READ and SOCST
t = 11.163, df = 198, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.5282957 0.6998781
sample estimates:
      cor 
0.6214843

Analysis: Linear Regression

Following this SPSS example: http://www.ats.ucla.edu/stat/spss/output/reg_spss.htm

hsb <- read.spss("c:/chuck/NYU/Stat Group/R Workshop/hsb2.sav", to.data.frame=TRUE)
head(hsb)

   ID FEMALE  RACE    SES SCHTYP     PROG READ WRITE MATH SCIENCE SOCST
1  70   male white    low public  general   57    52   41      47    57
2 121 female white middle public vocation   68    59   53      63    61
3  86   male white   high public  general   44    33   54      58    31
4 141   male white   high public vocation   63    44   47      53    56
5 172   male white middle public academic   47    52   57      53    61
6 113   male white middle public academic   44    52   51      63    61

Do the multiple regression analysis using the R lm() function (for linear model)

lm.1 <- lm(SCIENCE ~ MATH + FEMALE + SOCST + READ, data = hsb)

Summarize the fitted model

summary(lm.1)


Call:
lm(formula = SCIENCE ~ MATH + FEMALE + SOCST + READ, data = hsb)

Residuals:
     Min       1Q   Median       3Q      Max 
-18.6706  -4.5764  -0.3237   4.5006  21.8563 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  12.32529    3.19356   3.859 0.000154 ***
MATH          0.38931    0.07412   5.252 3.92e-07 ***
FEMALEfemale -2.00976    1.02272  -1.965 0.050820 .  
SOCST         0.04984    0.06223   0.801 0.424139    
READ          0.33530    0.07278   4.607 7.36e-06 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 7.148 on 195 degrees of freedom
Multiple R-squared:  0.4892,    Adjusted R-squared:  0.4788 
F-statistic: 46.69 on 4 and 195 DF,  p-value: < 2.2e-16

Confidence intervals

confint(lm.1)

                   2.5 %      97.5 %
(Intercept)   6.02694246 18.62363584
MATH          0.24312201  0.53549835
FEMALEfemale -4.02677214  0.00724283
SOCST        -0.07288986  0.17257843
READ          0.19176512  0.47883447

Analysis: Logistic Regression

Following this SPSS example: http://www.ats.ucla.edu/stat/spss/examples/alr2/alr2spssch2.html
Chapter 2, Applied Logistic Regression, Second Edition, by Hosmer and Lemeshow

Variable	Description	Codes/Values
LOW	Low Birth Weight	1 = BWT<=2500g, 0 = BWT>2500g
AGE	Age of Mother	Years
LWT	Weight of Mother at Last Menstrual Period	Pounds
RACE	Race	1=White, 2=Black, 3=Other
FTV	First Trimester Physician Visits	0=None, 1=One, 2=Two,etc.

R has a function, glm(), for generalized linear models, and logistic regression is a special case of generalized linear models
This is similar to the functionality of PROC GENMOD in SAS
Before modeling, we will read in the data and convert some variables to factors

lowbwt <- read.spss('c:/chuck/NYU/Stat Group/R Workshop/lowbwt.sav', to.data.frame=TRUE)
lowbwt$RACE <- car::recode(lowbwt$RACE, "1='White';2='Black';3='Other'", as.factor.result = TRUE)
lowbwt$RACE <- relevel(lowbwt$RACE, ref = 'White')

And now do the logistic regression

lrm.1 <- glm(LOW ~ AGE + LWT + RACE + FTV, family='binomial', data = lowbwt)

Summarize it

summary(lrm.1)


Call:
glm(formula = LOW ~ AGE + LWT + RACE + FTV, family = "binomial", 
    data = lowbwt)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.4163  -0.8931  -0.7113   1.2454   2.0755  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)  
(Intercept)  1.295366   1.071443   1.209   0.2267  
AGE         -0.023823   0.033730  -0.706   0.4800  
LWT         -0.014245   0.006541  -2.178   0.0294 *
RACEBlack    1.003898   0.497859   2.016   0.0438 *
RACEOther    0.433108   0.362240   1.196   0.2318  
FTV         -0.049308   0.167239  -0.295   0.7681  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 234.67  on 188  degrees of freedom
Residual deviance: 222.57  on 183  degrees of freedom
AIC: 234.57

Number of Fisher Scoring iterations: 4

Odds ratios and confidence intervals

exp(coef(lrm.1)[-1]) # Removing constant

      AGE       LWT RACEBlack RACEOther       FTV 
0.9764586 0.9858564 2.7288979 1.5420434 0.9518876

exp(confint(lrm.1)[-1,]) # Removing constant from confidence intervals too

              2.5 %    97.5 %
AGE       0.9124232 1.0421301
LWT       0.9725636 0.9979524
RACEBlack 1.0232217 7.3225967
RACEOther 0.7567464 3.1462285
FTV       0.6771508 1.3109248

Download and Plot Fitbit Data - All in R

library(fitbitScraper)
library(scales)
library(ggplot2)

source("c:/chuck/NYU/Stat Group/R Workshop/fitbit-cookie.r") 
df <- get_daily_data(cookie, what="distance", 
                     start_date="2014-01-01", 
                     end_date=as.character(Sys.Date()))

fig1 <- ggplot(subset(df, distance > 0), 
               aes(x=as.Date(time), y= distance, 
                   colour = as.Date(time) > "2015-01-01")) + 
  geom_point(size=2, shape=1) + ylab("Miles") + xlab("") + 
  stat_smooth(method='loess', se=FALSE) + 
  scale_y_continuous(breaks=seq(0,16,1)) +
  scale_x_date(breaks = date_breaks("2 months"), 
               labels = date_format("%b-%Y")) +
  scale_colour_manual(name = "After 1/1/15", 
                      values = c("#6B8E23","#8A2BE2")) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Extracting Data from Google Scholar

library(scholar)
library(purrr)

DF <- data.frame(Person = c("Chuck Cleland","Sam Friedman","Holly Hagan","Dani Ompad"),
                 GID = c("AuiR_rAAAAAJ","bQwCIGQAAAAJ","67LUQJUAAAAJ","Zxdn7DwAAAAJ"))

do.call(rbind, map(map(DF$GID, get_profile), 
                   function(x){data.frame(Person = x$name,
                                          Total.Cites = x$total_cites,
                                          h.index = x$h_index,
                                          i10.index = x$i10_index)}))

              Person Total.Cites h.index i10.index
1 Charles M. Cleland        3228      29        77
2  Samuel R Friedman       21374      78       295
3        Holly Hagan       16430      49       138
4     Danielle Ompad        3357      33        68

Generating Non-Random Data

seq(1, 10, 2)

[1] 1 3 5 7 9

104:114

 [1] 104 105 106 107 108 109 110 111 112 113 114

rep(c('A','B','C'), 3)

[1] "A" "B" "C" "A" "B" "C" "A" "B" "C"

rep(c('A','B','C'), each = 3)

[1] "A" "A" "A" "B" "B" "B" "C" "C" "C"

seq(1, 4, len = 6)

[1] 1.0 1.6 2.2 2.8 3.4 4.0

expand.grid(Person = 1:5, Visit = 1:3)

   Person Visit
1       1     1
2       2     1
3       3     1
4       4     1
5       5     1
6       1     2
7       2     2
8       3     2
9       4     2
10      5     2
11      1     3
12      2     3
13      3     3
14      4     3
15      5     3

Generating Random Data

rnorm(10)

 [1]  2.0665795 -2.7631017  0.1590837  0.3340059  0.9715169 -1.3661629
 [7] -0.4248921  0.2323954  0.4803709  1.4822548

runif(10, min=100,max=999)

 [1] 772.3865 812.8910 291.6639 813.0696 635.7335 919.6467 722.3682
 [8] 871.1754 202.0830 628.5196

rbinom(10, 1, .5)

 [1] 0 1 1 0 1 0 1 0 1 1

sample(letters[1:3], size = 30, replace=TRUE, prob = c(.8,.1,.1))

 [1] "a" "a" "a" "a" "a" "a" "b" "c" "a" "a" "a" "b" "a" "a" "b" "a" "a"
[18] "a" "a" "a" "b" "b" "b" "a" "a" "a" "b" "a" "c" "a"

sample(10)

 [1]  2  8  7  3  6  5  4  9  1 10

Where and how to find R packages

Comprehensive R Archive Network (CRAN; https://cran.r-project.org/mirrors.html)
CRAN task views (https://cloud.r-project.org/web/views/)
Google (e.g., “r-project psychometrics”)
http://www.rdocumentation.org/
R available.packages() function

How to install and load R packages

Install
- R install.packages() function
- Part of the RStudio and RWindows menus
Load a package to make content available to R
- R library() function

install.packages("epiR")

library(epiR)
head(ls("package:epiR"), n = 30)

 [1] "epi.2by2"         "epi.about"        "epi.asc"         
 [4] "epi.betabuster"   "epi.bohning"      "epi.ccc"         
 [7] "epi.ccsize"       "epi.cluster1size" "epi.cluster2size"
[10] "epi.clustersize"  "epi.cohortsize"   "epi.conf"        
[13] "epi.convgrid"     "epi.cp"           "epi.cpresids"    
[16] "epi.descriptives" "epi.detectsize"   "epi.dgamma"      
[19] "epi.directadj"    "epi.dms"          "epi.dsl"         
[22] "epi.edr"          "epi.empbayes"     "epi.equivb"      
[25] "epi.equivc"       "epi.herdtest"     "epi.indirectadj" 
[28] "epi.insthaz"      "epi.interaction"  "epi.iv"

Resources to Learn More

http://www.r-project.org/
https://www.datacamp.com/
http://www.ats.ucla.edu/stat/r/
http://www.rdocumentation.org/
Springer Use R! Series (http://www.springer.com/series/6991)
Cross Validated (http://stats.stackexchange.com/)
R-centric internet search (http://rseek.org/)

Local R Interest Group?

I am interested in supporting people trying to learn/use R
Let’s talk more about how and when

That’s it for today, and thank you!

Introduction to R

Get Started and See the Possibilities

Chuck Cleland

October 27, 2016

About me

Goals for Today

What is R?

R Windows

RStudio Integrated Development Enviornment

Why I use R and you should too

Basic Operations

Assignment

Evaluation

Getting help within R

Types of R Objects

Types of R variables in Data Frames

Vectorized Arithmetic

Make simple R objects

Access names and types of variables in a data frame

Accessing variables in data frames

The R Workspace

Getting data into R

read.table

read.spss

Read and combine many files

Getting data out of R

Data management: Recoding

Data management: Merging

Data management: Subsetting

Data management: Reshaping, Wide to Long

Data management: Reshaping, Long to Wide

Reshaping with data.table

Data management: Aggregating

Data Management: Set Operations

Data Management: Character Manipulation

Data Management: dplyr package

Houston flights data

Using filter() to select observations

Using select() to select variables

Using mutate() to create new variables

Using summarize()

Using group_by() and summarize() to summarize by group

Notice group_by() and summarize() essentially aggregate

Putting data into a desired order with arrange()

Benefits of Piping

Plots: Histogram

Plots: Bar

Plots: Scatter

Plots: Scatter Enhanced

Plots: Scatter with Facets

Plots: Boxplots

Plots: Alpha Transparency

Plots: Networks

Plots: Maps

Plots: More customized

Analysis: descriptive statistics

Analysis: frequencies

Analysis: crosstabulation

More detailed descriptive statistics for quantitative variables

Analysis: t-tests

Analysis: Correlation

Analysis: Linear Regression

Analysis: Logistic Regression

Download and Plot Fitbit Data - All in R

Extracting Data from Google Scholar

Generating Non-Random Data

Generating Random Data

More on R Packages

Where and how to find R packages

How to install and load R packages

Resources to Learn More

Local R Interest Group?

That’s it for today, and thank you!